6979results about How to "Reduce the burden on" patented technology

The invention discloses an exhibition hall robot visual language navigation method based on a large model, belongs to the field of exhibition hall robots, and aims at solving the problems existing in an existing exhibition hall robot. According to the method, an exhibition hall robot multi-mode scene sensing system senses a current scene, and candidate target objects are obtained through fusion features of RGB images and laser point clouds; extracting image features of all candidate target objects in the scene; according to the exhibition hall robot multi-mode environment understanding system, an exhibition hall robot receives a voice instruction, a visual language multi-mode large model is used for understanding a target object and a user intention, then high-dimensional semantic features of a target object text are extracted from the system, and the target object is found out from candidate target objects; according to the exhibition hall robot intelligent navigation system based on the multi-modal large model, an exhibition hall robot carries out path planning and autonomous movement according to a pose sequence generated by an intention, and self behaviors are dynamically adjusted according to a real-time changing scene provided by the exhibition hall robot multi-modal scene sensing system.

The invention relates to the technical field of electric power design, in particular to an intelligent dispatching system of a transformer area power supply grid. According to the method, a self-adaptive sampling strategy of real-time load fluctuation and distributed energy output characteristics is adopted; when the system detects the rapid change or abnormal fluctuation of the load, the data acquisition frequency is automatically improved, so that the capture of key information is ensured; when the load is stable, the sampling frequency is reduced, and the collection of redundant data is reduced; adaptive sampling can dynamically adjust the acquisition frequency, it is guaranteed that important data are not lost at the critical moment, and meanwhile the system burden is reduced; a hierarchical data processing architecture is adopted, data processing is divided into an edge layer and a cloud layer, the edge layer is responsible for conducting primary processing on data collected in a transformer area, including denoising, feature extraction and the like, and the data quality is guaranteed; only the key data subjected to primary processing is uploaded to a cloud end for data analysis and decision optimization; therefore, the burden of data transmission and processing is reduced, and the real-time response capability of the scheduling system is improved.

The invention provides an intrusion detection and response method and system for a satellite internet target range, and relates to the technical field of network security technologies, and the method comprises the steps: carrying out the multi-dimensional real-time collection of the network flow of the satellite internet target range, carrying out the self-adaptive multi-scale feature extraction, constructing a network behavior baseline model, and positioning and recognizing the abnormal flow. Deploying an anomaly detection node based on edge calculation, performing preliminary screening at a track end, performing multi-angle association, and outputting an anomaly event report; a distributed intelligent honeypot is activated, deep interaction is induced, the information collection amount is maximized, multi-modal data is obtained, and multi-dimensional attack feature vectors and behavior sequences are generated; historical defense empirical data are integrated, a method of combining reinforcement learning and a genetic algorithm is utilized, a defense strategy is coordinated, a defense process is generated, and the defense process is recorded through a block chain and fed back to a network behavior baseline model.

The invention provides a bridge girder erection machine intelligent construction control method and system based on machine vision, and relates to the technical field of intelligent control. Image information of a construction site is collected through a plurality of high-definition cameras arranged on a bridge girder erection machine; inputting the image information into a pre-trained deep learning model, identifying and positioning a target in the construction scene, and constructing a digital twinborn model of the construction scene in combination with a graph structure of a construction scene topological structure subjected to spatial perception graph convolution operation; the intelligent decision agent generates an initial construction strategy according to the current construction scene state, an improved Monte Carlo tree is adopted to search and optimize the initial construction strategy, and an optimal construction strategy is generated through multi-objective solution; and after the construction is completed, based on the digital twin model and the whole process data, analyzing the construction quality and efficiency by using a graph neural network, and generating a multi-dimensional evaluation report and optimization suggestions.

The invention relates to a health monitoring and fault prediction technology of intelligent equipment, in particular to an equipment fault prediction method based on health degree analysis of the intelligent equipment. According to the method, multiple sensors are embedded in the intelligent device, data of the operation state of the device are continuously collected, and primary processing and abnormal value elimination are carried out through an edge computing node. The processed data is transmitted to a cloud server, and machine learning models such as a deep neural network, a Bayesian classifier and a support vector machine are utilized to analyze the equipment health degree trend, and a potential fault mode is identified. And intelligently generating a fault prediction report according to an analysis result, and notifying an operation and maintenance team to take measures through an operation and maintenance management system. In addition, the system further comprises a historical fault database, a scoring system, an online review group and other functional modules, the accuracy and reliability of prediction are ensured, and the equipment maintenance efficiency and safety are improved.

The invention discloses a poultry breeding monitoring management system based on big data, and relates to the technical field of poultry breeding supervision, the poultry breeding monitoring management system monitors the environment in a poultry house in real time, immediately triggers a behavior monitoring module when abnormality is detected, generates a specific environmental abnormality score, and combines visual monitoring and infrared thermal imaging to realize the monitoring and management of poultry breeding. According to the method, individual behavior characteristics of each poultry are effectively extracted, an individual behavior data stream is generated, individual potential health risks are identified, a health archive is adopted to accumulate long-term behaviors and environmental data of each poultry, and a time sequence analysis model is utilized to identify individual behavior modes and health change trends. Possible health risks are predicted based on historical data and models, management perspectiveness and disease prevention and control efficiency are effectively improved, meanwhile, abnormal features with the most influence are automatically recognized through comprehensive risk scoring and contribution degree analysis and are pushed to management personnel, the health archive of each poultry is updated in real time, and the management efficiency is improved. And the overall operation efficiency of the farm is improved.

The invention provides a first-aid platform work order management method based on digital twinning and artificial intelligence, and belongs to the technical field of information technology application innovation, and the method comprises the steps: collecting historical first-aid data and real-time data of a first-aid scene, and constructing a task scheduling system based on a knowledge graph; decomposing the task into a plurality of sub-tasks according to the work order data, and allocating an optimal first-aid resource combination for the sub-tasks; an optimization strategy is set to carry out collaborative allocation control on each resource, and task allocation is optimized; the optimized task allocation is monitored in real time, and meanwhile, in combination with real-time data flow monitored in real time, it is ensured that the change of the dynamic environment is responded at any time in the task execution process; and re-inputting the optimized task allocation and resource scheduling strategy into the knowledge graph, and carrying out closed-loop optimization. According to the invention, the problems of inaccurate task scheduling, weak real-time response capability, low resource utilization rate and the like in the existing work order management system of the emergency platform are effectively overcome, and the overall efficiency and the automation level of the emergency platform are greatly improved.

The invention relates to the technical field of industrial automatic control, and discloses an electric control cabinet internal intelligent layout optimization and thermal management system, which comprises an intelligent layout optimization module, which adopts a particle swarm optimization algorithm to comprehensively analyze the multi-dimensional parameter size, the weight, the heat dissipation requirement and the like of internal equipment of an electric control cabinet, realizes layout optimization, and provides a thermal management module for the internal equipment of the electric control cabinet; the method comprises the following steps: receiving equipment parameters input by a user or automatically extracted from an equipment database, including but not limited to physical sizes, power consumption, thermal loads and the like of the equipment, and optimizing targets, including maximizing a space utilization rate, minimizing heat dissipation obstruction, optimizing a maintenance path and ensuring a safe distance between the equipment. According to the internal intelligent layout optimization and heat management system of the electric control cabinet, by comprehensively applying artificial intelligence, three-dimensional modeling, a multi-element heat dissipation technology and an Internet of Things technology, optimal layout and efficient heat management of internal equipment of the electric control cabinet are achieved, and the operation efficiency, safety and maintainability of the electric control cabinet are remarkably improved.

The invention discloses an Internet of Vehicles multi-parameter monitoring method and system based on 5G multi-access edge computing. The method comprises the following steps: collecting multi-source heterogeneous data in real time by using a vehicle-mounted sensor; transmitting the collected multi-source heterogeneous data to the nearest edge computing node in real time by using a 5G network; preprocessing the multi-source heterogeneous data at an edge computing node; inputting the fused data into a local model deployed on an edge node, and carrying out real-time monitoring and real-time evaluation on a vehicle state and a driver state; the obtained evaluation result is uploaded to a cloud platform, and the cloud platform dynamically adjusts a driving assistance decision and a control instruction according to the vehicle driving working condition and the environment data to generate an optimal decision result; and issuing the optimal decision result to a vehicle-mounted execution unit through a 5G network. According to the method, key parameters such as the vehicle speed and the physiological state of a driver are monitored, and the real-time performance and accuracy of a monitoring system are improved.

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

The invention discloses an intelligent advertisement putting processing method and system based on user data, and relates to the technical field of data processing, and the method comprises the steps: putting an advertisement in an advertisement pool based on an edge device, collecting the video information during the advertisement playing period through the edge device, analyzing the stored video information, extracting the user feature information, and storing the user feature information in the advertisement pool; the user feature information is integrated into user feature vectors, the user feature vectors stored in the edge devices are uploaded to the cloud platform, the cloud platform analyzes the user feature vectors uploaded by the edge devices to generate user portrait information, and the user portrait information is displayed on the basis of the user portrait information generated by the cloud platform. And putting corresponding advertisements to the user groups with similar user portrait information, collecting feedback data, and dynamically optimizing the advertisements put by the edge devices and the user groups based on the feedback data. The method has the advantages that the data transmission delay and the cloud computing pressure are reduced through the cloud edge collaboration method.

The invention provides a forest fire prevention unmanned aerial vehicle patrol early warning system and method based on edge calculation, and relates to the technical field of unmanned aerial vehicle patrol early warning. The patrol early warning system comprises an unmanned aerial vehicle platform, a data acquisition module, a data processing and analysis module, a communication and early warning module and a ground control and scheduling platform. According to the invention, calculation and data storage are close to a data source through edge calculation, the data collected by the unmanned aerial vehicle is processed in real time at an edge calculation node without being transmitted to a remote cloud and returning a result, the data processing and response time is greatly shortened, fire hazards can be found in seconds or even shorter time, and early warning can be given out. And the system can analyze data in real time in the flight process of the unmanned aerial vehicle, provides real-time decision support for a ground control center, and can quickly formulate a fire-fighting scheme according to information such as fire behavior and wind direction when a fire occurs, so that the fire-fighting efficiency is improved.

The invention provides a PCBA surface defect detection method and system based on image recognition, and the method comprises the steps: collecting and constructing a multi-class PCBA overall defect image data set, and carrying out the preprocessing; constructing a pre-training neural network model, and classifying the preprocessed PCBA overall defect image data set; constructing a local target identification method based on a YOLOX network according to an overall defect classification result; and carrying out local defect detection on the PCBA surface based on a local target identification method. According to the method, the overall classification and local detection strategies are comprehensively applied to reduce the burden of the model, and the detection precision and speed are improved. And an attention mechanism is introduced to improve the performance and generalization ability of the model. Data enhancement, consistency processing, classification labeling and the like are carried out on an image data set, and the defect detection effect of the model is improved. And multiple optimization strategies such as structure improvement, loss function improvement and pruning lightweight are adopted for the network model, so that the image target detection performance is effectively improved.

The present invention relates to the technical field of waste gas treatment, and discloses a low carbonic acid tower for tertiary carbonic acid production, the low carbonic acid tower comprises a tower body, the inner wall of the tower body is provided with a plurality of groups of support frames, the support frames are internally provided with filler layers, the lower parts of the support frames are provided with distribution plates, and trickling ports are uniformly distributed at the bottom of a spray pipe, so that the tertiary carbonic acid production efficiency is improved, and the tertiary carbonic acid production efficiency is improved. According to the waste gas absorption tower, through the arrangement of the filter layer, dripped absorption liquid can better cover the interior of the tower body, so that waste gas and the absorption liquid can be better in countercurrent contact, when the absorption liquid flows through the arranged filler layer, the contact area of the absorption liquid and the waste gas can be increased, and an adsorption sieve in the filler layer can also adsorb some harmful substances in the waste gas; and the distribution plate is arranged below the spraying pipe, so that the absorption liquid can be secondarily distributed and is uniformly distributed in the tower body again, and the condition of non-uniform distribution of liquid drops is avoided.

The invention provides an MBB multi-channel local aggregation superposition transmission method and an MBB multi-channel local aggregation superposition transmission system. Belongs to the technical field of communication. The method comprises the following steps that: a plurality of physical links are established in a wireless communication system, and the system selects the plurality of optimal physical links for aggregation according to a current network environment, a current equipment state and a current user demand; dividing data to be transmitted into a plurality of data packets, packaging each data packet according to the characteristics of the selected physical link, and transmitting the packaged data packets in parallel through the selected physical link; in the transmission process, the system monitors the transmission state of each link in real time, and dynamically adjusts the transmission rate and power distribution of each link according to the current network load and equipment state. By transmitting a plurality of physical links in parallel, the network bandwidth can be fully utilized, the data transmission rate is improved, and the transmission delay is reduced.

The invention discloses a wind power plant equipment fault intelligent diagnosis method and system. The method comprises the following steps: acquiring multi-dimensional sensor data of wind power plant equipment; carrying out preliminary screening on the data by utilizing an edge analysis algorithm, and determining data needing to be further diagnosed; the screened data are input into a pre-constructed fault diagnosis model, the data are fused through a multi-modal deep learning model in the pre-constructed fault diagnosis model, and then a fault occurrence area is determined through transfer learning, domain adaptation and a multi-agent reinforcement learning framework; meanwhile, the causal inference engine and the knowledge graph enhanced neural symbol inference module are responsible for deep processing and fault diagnosis. And finally, inputting the determined fault area and the diagnosis result into a dynamic system health index calculation module to generate a final fault diagnosis result. According to the method, the diagnosis efficiency and accuracy of the equipment fault of the wind power plant are remarkably improved, and a powerful guarantee is provided for stable operation of the wind power plant.

The invention relates to the technical field of training strategy optimization, in particular to an optimization method and system based on a rehabilitation training strategy. The method comprises the following steps: carrying out real-time monitoring on physiology and training performance of a patient in a rehabilitation training process through wearable equipment, and carrying out evaluation analysis on an emotional state and athletic ability to obtain emotional feeling state data and athletic ability data of the patient; performing rehabilitation target prediction analysis according to the emotion feeling state data and the exercise ability data to obtain a prediction target of rehabilitation training of the patient; performing target feedback difference comparative analysis on the predicted target and an expected target to obtain a target difference feedback result; performing strategy optimization analysis on the current rehabilitation training strategy to generate a rehabilitation training strategy optimization scheme; and performing strategy dynamic optimization adjustment on the current rehabilitation training strategy to obtain an optimized rehabilitation training strategy. The rehabilitation training efficiency and effect of the patient can be improved.

The invention discloses a multi-factor dynamic identity verification and access control system, which comprises a user management module, a biological recognition module, a strategy-level authorization module, a dynamic evaluation module and an interface module, and is characterized in that the user management module is used for user registration, information management and logout; the biological recognition module adopts a biological recognition technology to collect, process and compare biological characteristics of a user; the strategy-level authorization module is used for accessing sensitivity levels of applications and terminal type factors according to user identities; the dynamic evaluation module is responsible for monitoring and evaluating the user access process in real time; and the interface module is used for providing an interactive interface between the system and other systems or applications. By integrating various advanced technologies, efficient, safe and flexible access control management is realized, and the safety and user experience of the whole system are improved.

The invention relates to the technical field of sampling, in particular to a portable sampling device for geological mineral exploration and a sampling method thereof.The portable sampling device comprises a bottom plate, guide frames, a lifting plate and the like, the two guide frames are connected to the top of the bottom plate, and the lifting plate is jointly connected to the two guide frames in a sliding mode. A rock core drill bit can be driven to move downwards through a screw rod motor, the rock core drill bit drills into soil, the soil enters a sampling pipe for sampling, a standby sampling pipe can be driven to move backwards through an electric guide rail, the standby sampling pipe is moved to the position below a mounting pipe, and the sampling pipe is spliced; the sampling pipe can be driven to move forwards through the electric guide rail, the sampling pipe can be disassembled, the sampling pipe can be automatically spliced and disassembled, operation is simpler, the burden of workers can be relieved, and therefore the sampling efficiency can be improved.

The invention discloses a security data generation method and device, a storage medium and an electronic device.The method comprises the steps that description information of a target task is obtained, the description information at least comprises a first task attribute corresponding to an equipment type associated with the target task, a second task attribute corresponding to an operation type associated with the target task, and a third task attribute corresponding to an operation step associated with the target task; the description information is used for filling placeholder information in a target template, multiple structured cue words are obtained, and the target template is a cue word template which is retrieved from a pre-constructed template library and related to the target task; and screening out a target cue word from the plurality of structured cue words, and inputting the target cue word into a pre-trained large language model to obtain security data corresponding to the target cue word. The problem of how to improve the generation quality and the generation efficiency of the safety measure data corresponding to the operation ticket and the work ticket is solved.

The invention provides an image rendering method and device, electronic equipment and a storage medium, and the method comprises the steps: calculating a distance between a virtual image obtaining device and a to-be-rendered object through determining the to-be-rendered object in a to-be-rendered scene, and determining the distance as a first loading factor; determining a rendering picture pixel proportion of each to-be-rendered object based on preset view cone size information, determining the rendering picture pixel proportion as a second loading factor, generating a rendering loading priority sequence according to the first loading factor, and determining a model level resource file and a texture level resource file according to the second loading factor, loading the resources to a video memory for rendering according to the sequence; the priority is determined by calculating the distance between the camera and the to-be-rendered object, it is ensured that a high-quality model and texture of a close-range object are loaded preferentially, the instantaneity of the rendering loading process is improved, texture and model grade division is conducted through the pixel proportion of the to-be-rendered object in the picture, and the rendering efficiency is improved. The occupation of a video memory is reduced, and the overall rendering performance of the system is improved.

The invention provides an intelligent cold chain transportation information management method, system and device and a storage medium, and the method comprises the steps: recognizing the type of transported goods and environment demands through a task recognition module, and determining and configuring a target sensor of a cold chain vehicle through a sensor configuration module; the target sensor collects environment data, the vehicle-mounted GPS module collects position information and a driving state, the RFID tag records a cargo loading state, and all data are sent to the central management platform. The platform constructs a multi-dimensional feature vector, the multi-dimensional feature vector is compressed into a low-dimensional feature representation through an encoder, a reconstruction data vector is obtained through reconstruction of a decoder, and a reconstruction error is calculated. And by analyzing the reconstruction error, the platform generates an analysis result, and the environment control equipment in the vehicle is automatically adjusted according to the result, so that the stability of cargo transportation is ensured.

The invention discloses a personalized hypertensive nephropathy risk assessment method and system based on a graph convolutional network, and the method comprises the steps: collecting modal data related to hypertensive nephropathy, and carrying out the preprocessing; constructing a patient similarity graph; performing multi-source information fusion and domain knowledge integration; sparse processing is carried out on the graph structure, and standardization processing is carried out on an adjacent matrix; modeling through a time sequence diagram convolutional network and a long-short term memory network, and capturing the evolution trend of patient characteristics along with time; through a self-attention mechanism and a graph regularization item, the transmission of important features is enhanced; a multi-head attention mechanism is adopted, and different types of relation modes are captured; optimizing model parameters by using an Adam optimization algorithm and a regularization method; and a risk assessment result is optimized by adopting weighted stacking and a self-adaptive attention mechanism. According to the risk assessment method, accurate assessment of the risk of hypertensive nephropathy is realized by constructing the multi-dimensional and dynamically changing patient similarity graph.

The invention belongs to the field of test case generation in traditional software engineering, and discloses a Python program test case generation method based on a large language model. The method can automatically generate and optimize a unit test case of a Python program, and comprises the following steps of: extracting public basic context and function information by utilizing project dependent structure scanning and an AST abstract syntax tree, and constructing an accurate cue word template; verification of grammar, operation logic and coverage rate is carried out on the test case generated by the large language model; aiming at the failed test case, designing a new cue word template and an iteration process so as to automatically iterate and improve, and setting the maximum number of iterations so as to ensure the efficiency; and finally, outputting an optimized high-quality successful test case set so as to improve the test efficiency and reduce the waste of manpower and material resources.

According to the technical scheme, the BIM model Web end lightweight display method comprises the following steps that S1, a project drawing is created; s2, modeling by using BIM software, and constructing a model hierarchical structure; s3, performing multi-level integration compression on the BIM model; s4, the lightweight BIM model is imported into Unity, and rendering is carried out; s5, UI interface element design is carried out by using a Unity UI; s6, a WebGL platform is set in the Unity, and WebGL items are packaged; and S7, deploying the file to a cloud server. The method has the beneficial effects that multi-level integration lightweight is adopted, and the integrity of geometric data and material texture information of the BIM model is guaranteed. The rendering detail level of the model is adjusted through Lod multi-level detail management built in Unity, and the rendering burden is reduced while the quality of the BIM model is ensured. And by utilizing a Unity asynchronous loading function, the transmission data volume is effectively reduced, and the loading speed and the rendering performance are improved.

The invention provides a meteorological monitoring method based on a GRU neural network and differential privacy federated learning. The meteorological monitoring method comprises the following steps: collecting local meteorological data to generate a meteorological time sequence data sequence; selecting a proper model structure according to the heterogeneity of a client, carrying out local meteorological monitoring model training based on a GRU neural network, and carrying out parameter updating on a local model through an LPDP-FedProx federal learning algorithm; the central server performs model aggregation and updating according to a global model weighted average aggregation algorithm after acquiring model parameters of the client; and the client receives the updated global model parameters and carries out the next round of local training until an optimal monitoring model is obtained. According to the method, the personalized differential privacy algorithm and the FedProx federated learning algorithm are combined, the LPDP-FedProx federated learning algorithm is provided, a federated learning framework capable of effectively processing heterogeneous data is constructed, the privacy of local data is effectively protected, and the security of federated learning is improved.

The invention relates to the technical field of irrigation control, in particular to a water and fertilizer integrated irrigation control method based on a wheat growth cycle, which comprises the following steps: collecting soil humidity and wheat growth state data, and screening key soil and biological parameters influencing wheat growth by applying a random forest algorithm; and predicting the requirements of the wheat on water and nutrition in the differential growth stage according to the key parameters. According to the method, the water and nutrition requirements of wheat in different growth stages are accurately predicted by adopting a random forest algorithm, resource allocation is directly carried out according to the actual needs of plants, the problems of resource waste and insufficient supply of a traditional irrigation system are effectively avoided, and a dynamic adjustment strategy is based on current soil and plant states and also based on a dynamic adjustment strategy. And a water and fertilizer supply plan is optimized by using historical and predicted data, the capability of responding to environmental changes is enhanced, irrigation and fertilization parameters are finely adjusted by combining real-time feedback circulation with a PID controller, and the accuracy and effectiveness of each time of irrigation and fertilization are further ensured.

The present invention relates to the technical field of microfluidics, and discloses a microfluidic chip, which comprises a mixing detection module, a control valve and a power module, and the mixing detection module comprises a plurality of storage cavities, a first channel, a second channel, a third channel, a common channel, a mixing cavity and a detection cavity. According to the invention, the storage cavity for providing the reagent, the channel for fluid such as the reagent and the sample to flow, the mixing cavity for mixing and reacting the fluid such as the reagent and the sample, and the detection cavity for analyzing and detecting the sample are integrated on the mixing detection module; a control valve and a power module which are used for realizing directional flow and mixed reaction of fluid in each channel are arranged as parts which can be removed relative to the mixed detection module, so that the integration degree of the micro-fluidic chip can be improved, and the volume of the micro-fluidic chip can be reduced; in addition, the installation and replacement of the mixed detection module serving as a consumable are facilitated, and the flexibility and convenience of the micro-fluidic chip in use are effectively improved.

The invention relates to the technical field of data transmission and updating, in particular to a data transmission and updating system and method of a cloud mobile phone. According to the technical scheme, the system comprises an intelligent network monitoring module, a dynamic bandwidth allocation module, a server load balancing module, an intelligent data synchronization module and a cache management module, and the intelligent network monitoring module monitors network conditions, including network bandwidth, network congestion degree and network stability, between a user terminal and a cloud server in real time. Through the synergistic effect of the intelligent network monitoring module, the dynamic bandwidth allocation module, the server load balancing module, the intelligent data synchronization module, the cache management module and the like, the network condition is accurately monitored, the bandwidth is reasonably allocated, the server load is optimized, the data synchronization and completeness are ensured, and the cache efficiency is improved; stable and reliable network connection, preferential key data transmission and rapid data synchronization and loading are brought to users, and the use experience of the cloud mobile phone and the overall performance of the system are greatly improved.

The invention provides an intelligent refrigeration system efficiency optimization method, system and device based on deep learning, and relates to the technical field of intelligent refrigeration system efficiency optimization. The method comprises the following steps: firstly, collecting historical operation parameters and environmental data of the refrigeration system, preprocessing, and integrating into a sample set according to a timestamp; and based on the hybrid model of the convolutional neural network and the long-short term memory network, constructing a state prediction model for predicting the system operation state. And in combination with the state prediction model, an optimization objective function is defined for data prediction at the next future moment, and an optimal adjustment strategy for system operation is formulated by using a deep Q network model. And finally, monitoring current operation parameters and environment data in real time, calculating a comprehensive safety score, comparing the comprehensive safety score with a safety threshold value, and deciding whether to continue to optimize the efficiency or preferentially carry out troubleshooting. According to the method, through combination of deep learning and physical modeling, efficient and safe refrigeration system operation optimization is realized.